Overview

Dataset statistics

Number of variables14
Number of observations5570
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory189.9 B

Variable types

Numeric13
Categorical1

Alerts

Município has a high cardinality: 5570 distinct values High cardinality
CV_HEPatite_BB is highly correlated with CV_HIB and 9 other fieldsHigh correlation
CV_HIB is highly correlated with CV_HEPatite_BB and 9 other fieldsHigh correlation
CV_DPT is highly correlated with CV_HEPatite_BB and 9 other fieldsHigh correlation
CV_POLIO is highly correlated with CV_HEPatite_BB and 9 other fieldsHigh correlation
CV_ROTA is highly correlated with CV_HEPatite_BB and 9 other fieldsHigh correlation
CV_PNEMO is highly correlated with CV_HEPatite_BB and 9 other fieldsHigh correlation
CV_MnCC is highly correlated with CV_HEPatite_BB and 9 other fieldsHigh correlation
CV_SCR1 is highly correlated with CV_HEPatite_BB and 9 other fieldsHigh correlation
CV_SCR2 is highly correlated with CV_HEPatite_BB and 9 other fieldsHigh correlation
CV_Varicela is highly correlated with CV_HEPatite_BB and 9 other fieldsHigh correlation
CV_HEPatite_A is highly correlated with CV_HEPatite_BB and 9 other fieldsHigh correlation
Município is uniformly distributed Uniform
COD has unique values Unique
Município has unique values Unique
CV_BCG has 177 (3.2%) zeros Zeros

Reproduction

Analysis started2022-11-09 00:41:13.926584
Analysis finished2022-11-09 00:41:42.031648
Duration28.11 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

COD
Real number (ℝ≥0)

UNIQUE

Distinct5570
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean325358.6278
Minimum110001
Maximum530010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:41:42.199666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum110001
5-th percentile150777.25
Q1251212.5
median314627.5
Q3411918.75
95-th percentile510729.55
Maximum530010
Range420009
Interquartile range (IQR)160706.25

Descriptive statistics

Standard deviation98491.03388
Coefficient of variation (CV)0.3027152977
Kurtosis-0.5258091553
Mean325358.6278
Median Absolute Deviation (MAD)74152.5
Skewness0.1213411839
Sum1812247557
Variance9700483754
MonotonicityNot monotonic
2022-11-08T21:41:42.456665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100011
 
< 0.1%
3539701
 
< 0.1%
3540401
 
< 0.1%
3540301
 
< 0.1%
3540251
 
< 0.1%
3540201
 
< 0.1%
3540101
 
< 0.1%
3540001
 
< 0.1%
3539901
 
< 0.1%
3539801
 
< 0.1%
Other values (5560)5560
99.8%
ValueCountFrequency (%)
1100011
< 0.1%
1100021
< 0.1%
1100031
< 0.1%
1100041
< 0.1%
1100051
< 0.1%
1100061
< 0.1%
1100071
< 0.1%
1100081
< 0.1%
1100091
< 0.1%
1100101
< 0.1%
ValueCountFrequency (%)
5300101
< 0.1%
5222301
< 0.1%
5222201
< 0.1%
5222051
< 0.1%
5222001
< 0.1%
5221901
< 0.1%
5221851
< 0.1%
5221801
< 0.1%
5221701
< 0.1%
5221601
< 0.1%

Município
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct5570
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size467.3 KiB
110001 Alta Floresta D'Oeste
 
1
353970 Platina
 
1
354040 Populina
 
1
354030 Pontes Gestal
 
1
354025 Pontalinda
 
1
Other values (5565)
5565 

Length

Max length39
Median length34
Mean length18.61059246
Min length10

Characters and Unicode

Total characters103661
Distinct characters80
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5570 ?
Unique (%)100.0%

Sample

1st row110001 Alta Floresta D'Oeste
2nd row110002 Ariquemes
3rd row110003 Cabixi
4th row110004 Cacoal
5th row110005 Cerejeiras

Common Values

ValueCountFrequency (%)
110001 Alta Floresta D'Oeste1
 
< 0.1%
353970 Platina1
 
< 0.1%
354040 Populina1
 
< 0.1%
354030 Pontes Gestal1
 
< 0.1%
354025 Pontalinda1
 
< 0.1%
354020 Pontal1
 
< 0.1%
354010 Pongaí1
 
< 0.1%
354000 Pompéia1
 
< 0.1%
353990 Poloni1
 
< 0.1%
353980 Poá1
 
< 0.1%
Other values (5560)5560
99.8%

Length

2022-11-08T21:41:42.591664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
do756
 
4.8%
são364
 
2.3%
de302
 
1.9%
santa161
 
1.0%
da143
 
0.9%
nova135
 
0.9%
sul115
 
0.7%
rio94
 
0.6%
dos73
 
0.5%
josé70
 
0.4%
Other values (9533)13640
86.0%

Most occurring characters

ValueCountFrequency (%)
10283
 
9.9%
a8791
 
8.5%
08160
 
7.9%
o5961
 
5.8%
14774
 
4.6%
24591
 
4.4%
r4532
 
4.4%
i4388
 
4.2%
34106
 
4.0%
e3764
 
3.6%
Other values (70)44311
42.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter50872
49.1%
Decimal Number33420
32.2%
Space Separator10283
 
9.9%
Uppercase Letter9010
 
8.7%
Other Punctuation47
 
< 0.1%
Dash Punctuation29
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a8791
17.3%
o5961
11.7%
r4532
8.9%
i4388
8.6%
e3764
 
7.4%
n3196
 
6.3%
d2553
 
5.0%
s2423
 
4.8%
t2293
 
4.5%
u2155
 
4.2%
Other values (27)10816
21.3%
Uppercase Letter
ValueCountFrequency (%)
S1137
12.6%
C970
10.8%
P911
 
10.1%
M721
 
8.0%
A698
 
7.7%
B602
 
6.7%
I475
 
5.3%
J405
 
4.5%
G391
 
4.3%
R367
 
4.1%
Other values (20)2333
25.9%
Decimal Number
ValueCountFrequency (%)
08160
24.4%
14774
14.3%
24591
13.7%
34106
12.3%
53654
10.9%
42781
 
8.3%
71470
 
4.4%
61422
 
4.3%
91382
 
4.1%
81080
 
3.2%
Space Separator
ValueCountFrequency (%)
10283
100.0%
Other Punctuation
ValueCountFrequency (%)
'47
100.0%
Dash Punctuation
ValueCountFrequency (%)
-29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin59882
57.8%
Common43779
42.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a8791
14.7%
o5961
 
10.0%
r4532
 
7.6%
i4388
 
7.3%
e3764
 
6.3%
n3196
 
5.3%
d2553
 
4.3%
s2423
 
4.0%
t2293
 
3.8%
u2155
 
3.6%
Other values (57)19826
33.1%
Common
ValueCountFrequency (%)
10283
23.5%
08160
18.6%
14774
10.9%
24591
10.5%
34106
 
9.4%
53654
 
8.3%
42781
 
6.4%
71470
 
3.4%
61422
 
3.2%
91382
 
3.2%
Other values (3)1156
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII100822
97.3%
None2839
 
2.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10283
 
10.2%
a8791
 
8.7%
08160
 
8.1%
o5961
 
5.9%
14774
 
4.7%
24591
 
4.6%
r4532
 
4.5%
i4388
 
4.4%
34106
 
4.1%
e3764
 
3.7%
Other values (54)41472
41.1%
None
ValueCountFrequency (%)
ã794
28.0%
á393
13.8%
í336
11.8%
é317
 
11.2%
ç268
 
9.4%
ó243
 
8.6%
â161
 
5.7%
ú101
 
3.6%
ô71
 
2.5%
ê70
 
2.5%
Other values (6)85
 
3.0%

CV_BCG
Real number (ℝ≥0)

ZEROS

Distinct3660
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.32931418
Minimum0
Maximum798.5
Zeros177
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:41:42.722665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.0645
Q145.83
median80.175
Q397.4625
95-th percentile125.8895
Maximum798.5
Range798.5
Interquartile range (IQR)51.6325

Descriptive statistics

Standard deviation42.3616947
Coefficient of variation (CV)0.5776911345
Kurtosis34.29976691
Mean73.32931418
Median Absolute Deviation (MAD)22.145
Skewness2.489903005
Sum408444.28
Variance1794.513178
MonotonicityNot monotonic
2022-11-08T21:41:42.858662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0177
 
3.2%
100100
 
1.8%
5019
 
0.3%
7516
 
0.3%
83.3314
 
0.3%
2513
 
0.2%
66.6712
 
0.2%
90.9110
 
0.2%
94.4410
 
0.2%
8010
 
0.2%
Other values (3650)5189
93.2%
ValueCountFrequency (%)
0177
3.2%
0.451
 
< 0.1%
0.481
 
< 0.1%
0.552
 
< 0.1%
0.671
 
< 0.1%
0.72
 
< 0.1%
0.792
 
< 0.1%
0.811
 
< 0.1%
0.821
 
< 0.1%
0.841
 
< 0.1%
ValueCountFrequency (%)
798.51
< 0.1%
727.31
< 0.1%
572.661
< 0.1%
548.771
< 0.1%
464.741
< 0.1%
431.911
< 0.1%
391.941
< 0.1%
268.161
< 0.1%
265.941
< 0.1%
262.351
< 0.1%

CV_HEPatite_BB
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3422
Distinct (%)61.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.21601975
Minimum0
Maximum361.54
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:41:42.994663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile46.469
Q167.7075
median81.39
Q397.73
95-th percentile134.78
Maximum361.54
Range361.54
Interquartile range (IQR)30.0225

Descriptive statistics

Standard deviation29.34078907
Coefficient of variation (CV)0.3443107195
Kurtosis8.500584716
Mean85.21601975
Median Absolute Deviation (MAD)14.925
Skewness1.744074483
Sum474653.23
Variance860.8819035
MonotonicityNot monotonic
2022-11-08T21:41:43.129661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10084
 
1.5%
7525
 
0.4%
77.7819
 
0.3%
8018
 
0.3%
66.6717
 
0.3%
83.3315
 
0.3%
71.4314
 
0.3%
84.6214
 
0.3%
93.7513
 
0.2%
9013
 
0.2%
Other values (3412)5338
95.8%
ValueCountFrequency (%)
02
< 0.1%
1.171
< 0.1%
1.591
< 0.1%
4.351
< 0.1%
6.451
< 0.1%
8.621
< 0.1%
9.441
< 0.1%
9.91
< 0.1%
9.911
< 0.1%
11.631
< 0.1%
ValueCountFrequency (%)
361.541
< 0.1%
3501
< 0.1%
340.741
< 0.1%
316.671
< 0.1%
277.781
< 0.1%
272.221
< 0.1%
265.121
< 0.1%
264.291
< 0.1%
263.641
< 0.1%
261.111
< 0.1%

CV_HIB
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3431
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.31403052
Minimum0
Maximum361.54
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:41:43.261662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile46.5115
Q167.8325
median81.43
Q397.92
95-th percentile134.857
Maximum361.54
Range361.54
Interquartile range (IQR)30.0875

Descriptive statistics

Standard deviation29.34923618
Coefficient of variation (CV)0.3440141791
Kurtosis8.476601842
Mean85.31403052
Median Absolute Deviation (MAD)14.91
Skewness1.740420109
Sum475199.15
Variance861.3776641
MonotonicityNot monotonic
2022-11-08T21:41:43.396663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10076
 
1.4%
7525
 
0.4%
8018
 
0.3%
77.7817
 
0.3%
66.6717
 
0.3%
83.3315
 
0.3%
71.4315
 
0.3%
84.6214
 
0.3%
85.7114
 
0.3%
93.7513
 
0.2%
Other values (3421)5346
96.0%
ValueCountFrequency (%)
02
< 0.1%
1.171
< 0.1%
1.591
< 0.1%
4.351
< 0.1%
6.451
< 0.1%
8.621
< 0.1%
9.441
< 0.1%
9.91
< 0.1%
9.911
< 0.1%
11.631
< 0.1%
ValueCountFrequency (%)
361.541
< 0.1%
3501
< 0.1%
340.741
< 0.1%
316.671
< 0.1%
277.781
< 0.1%
272.221
< 0.1%
265.121
< 0.1%
264.291
< 0.1%
263.641
< 0.1%
261.111
< 0.1%

CV_DPT
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3431
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.49964632
Minimum0
Maximum361.54
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:41:43.538664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile46.679
Q167.925
median81.7
Q398.1675
95-th percentile135
Maximum361.54
Range361.54
Interquartile range (IQR)30.2425

Descriptive statistics

Standard deviation29.39396865
Coefficient of variation (CV)0.3437905292
Kurtosis8.490359389
Mean85.49964632
Median Absolute Deviation (MAD)14.885
Skewness1.746049892
Sum476233.03
Variance864.0053932
MonotonicityNot monotonic
2022-11-08T21:41:43.672662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10073
 
1.3%
7524
 
0.4%
8018
 
0.3%
77.7816
 
0.3%
66.6716
 
0.3%
83.3316
 
0.3%
85.7113
 
0.2%
71.4313
 
0.2%
69.2313
 
0.2%
91.6712
 
0.2%
Other values (3421)5356
96.2%
ValueCountFrequency (%)
02
< 0.1%
1.171
< 0.1%
1.591
< 0.1%
4.351
< 0.1%
6.451
< 0.1%
8.621
< 0.1%
9.441
< 0.1%
9.911
< 0.1%
10.11
< 0.1%
11.631
< 0.1%
ValueCountFrequency (%)
361.541
< 0.1%
3501
< 0.1%
340.741
< 0.1%
316.671
< 0.1%
277.781
< 0.1%
272.221
< 0.1%
265.121
< 0.1%
264.291
< 0.1%
263.642
< 0.1%
261.111
< 0.1%

CV_POLIO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3362
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.47475763
Minimum0
Maximum715.38
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:41:43.804663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile59.7445
Q182.425
median94.29
Q3107.815
95-th percentile145.088
Maximum715.38
Range715.38
Interquartile range (IQR)25.39

Descriptive statistics

Standard deviation29.39536708
Coefficient of variation (CV)0.3015690195
Kurtosis42.60275208
Mean97.47475763
Median Absolute Deviation (MAD)12.68
Skewness3.217633832
Sum542934.4
Variance864.0876058
MonotonicityNot monotonic
2022-11-08T21:41:43.937663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100133
 
2.4%
133.3320
 
0.4%
8018
 
0.3%
7518
 
0.3%
128.5717
 
0.3%
12515
 
0.3%
85.7114
 
0.3%
114.2913
 
0.2%
116.6713
 
0.2%
83.3313
 
0.2%
Other values (3352)5296
95.1%
ValueCountFrequency (%)
03
0.1%
4.351
 
< 0.1%
13.511
 
< 0.1%
13.581
 
< 0.1%
13.741
 
< 0.1%
13.791
 
< 0.1%
15.621
 
< 0.1%
16.071
 
< 0.1%
16.111
 
< 0.1%
16.131
 
< 0.1%
ValueCountFrequency (%)
715.381
< 0.1%
3751
< 0.1%
3601
< 0.1%
337.041
< 0.1%
304.121
< 0.1%
285.751
< 0.1%
2751
< 0.1%
272.221
< 0.1%
2701
< 0.1%
266.672
< 0.1%

CV_ROTA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3258
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.8920395
Minimum0
Maximum425
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:41:44.070661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile62.63
Q184.7325
median95.62
Q3107.9275
95-th percentile140
Maximum425
Range425
Interquartile range (IQR)23.195

Descriptive statistics

Standard deviation25.70106644
Coefficient of variation (CV)0.2625450095
Kurtosis11.32207726
Mean97.8920395
Median Absolute Deviation (MAD)11.52
Skewness1.670716931
Sum545258.66
Variance660.5448163
MonotonicityNot monotonic
2022-11-08T21:41:44.200661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100135
 
2.4%
114.2919
 
0.3%
111.1116
 
0.3%
12016
 
0.3%
116.6716
 
0.3%
87.515
 
0.3%
91.6714
 
0.3%
133.3313
 
0.2%
8012
 
0.2%
7512
 
0.2%
Other values (3248)5302
95.2%
ValueCountFrequency (%)
02
< 0.1%
1.591
< 0.1%
11.261
< 0.1%
12.071
< 0.1%
12.91
< 0.1%
13.161
< 0.1%
13.211
< 0.1%
16.981
< 0.1%
18.261
< 0.1%
19.051
< 0.1%
ValueCountFrequency (%)
4251
< 0.1%
311.112
< 0.1%
291.671
< 0.1%
276.921
< 0.1%
2701
< 0.1%
266.671
< 0.1%
263.161
< 0.1%
2501
< 0.1%
245.451
< 0.1%
241.31
< 0.1%

CV_PNEMO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3256
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.304325
Minimum0
Maximum433.33
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:41:44.329664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67.07
Q188.135
median98.425
Q3111.11
95-th percentile144.404
Maximum433.33
Range433.33
Interquartile range (IQR)22.975

Descriptive statistics

Standard deviation25.94379161
Coefficient of variation (CV)0.2560975715
Kurtosis12.33643329
Mean101.304325
Median Absolute Deviation (MAD)11.34
Skewness1.804307687
Sum564265.09
Variance673.080323
MonotonicityNot monotonic
2022-11-08T21:41:44.580661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100130
 
2.3%
116.6720
 
0.4%
111.1119
 
0.3%
133.3318
 
0.3%
11017
 
0.3%
12515
 
0.3%
112.515
 
0.3%
114.2914
 
0.3%
91.6713
 
0.2%
95.4512
 
0.2%
Other values (3246)5297
95.1%
ValueCountFrequency (%)
02
< 0.1%
3.171
< 0.1%
12.91
< 0.1%
13.061
< 0.1%
14.721
< 0.1%
16.981
< 0.1%
18.971
< 0.1%
19.191
< 0.1%
19.721
< 0.1%
21.931
< 0.1%
ValueCountFrequency (%)
433.331
< 0.1%
344.441
< 0.1%
323.081
< 0.1%
305.561
< 0.1%
291.671
< 0.1%
270.371
< 0.1%
265.791
< 0.1%
2601
< 0.1%
258.441
< 0.1%
2501
< 0.1%

CV_MnCC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3299
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.2620682
Minimum0
Maximum375
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:41:44.712662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile64.128
Q186.4125
median97.695
Q3110.53
95-th percentile145.607
Maximum375
Range375
Interquartile range (IQR)24.1175

Descriptive statistics

Standard deviation26.87693061
Coefficient of variation (CV)0.2680667883
Kurtosis9.675608158
Mean100.2620682
Median Absolute Deviation (MAD)11.935
Skewness1.614179705
Sum558459.72
Variance722.3693992
MonotonicityNot monotonic
2022-11-08T21:41:44.842661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100116
 
2.1%
12520
 
0.4%
116.6717
 
0.3%
111.1116
 
0.3%
112.515
 
0.3%
114.2915
 
0.3%
93.7514
 
0.3%
12013
 
0.2%
83.3313
 
0.2%
106.2511
 
0.2%
Other values (3289)5320
95.5%
ValueCountFrequency (%)
02
< 0.1%
1.591
< 0.1%
7.251
< 0.1%
13.061
< 0.1%
16.131
< 0.1%
16.61
< 0.1%
17.221
< 0.1%
18.331
< 0.1%
18.971
< 0.1%
19.011
< 0.1%
ValueCountFrequency (%)
3751
< 0.1%
362.961
< 0.1%
338.461
< 0.1%
304.171
< 0.1%
3001
< 0.1%
277.781
< 0.1%
270.371
< 0.1%
268.181
< 0.1%
266.231
< 0.1%
254.351
< 0.1%

CV_SCR1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3245
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.9138025
Minimum0
Maximum367.74
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:41:44.973661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile68.797
Q189.87
median100
Q3112.105
95-th percentile148.0855
Maximum367.74
Range367.74
Interquartile range (IQR)22.235

Descriptive statistics

Standard deviation26.12121151
Coefficient of variation (CV)0.2538164063
Kurtosis10.80717523
Mean102.9138025
Median Absolute Deviation (MAD)11.11
Skewness1.741423626
Sum573229.88
Variance682.3176909
MonotonicityNot monotonic
2022-11-08T21:41:45.103663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100153
 
2.7%
133.3323
 
0.4%
12021
 
0.4%
11016
 
0.3%
90.9116
 
0.3%
9016
 
0.3%
116.6715
 
0.3%
92.8613
 
0.2%
105.5611
 
0.2%
128.5711
 
0.2%
Other values (3235)5275
94.7%
ValueCountFrequency (%)
02
< 0.1%
2.821
< 0.1%
4.61
< 0.1%
8.771
< 0.1%
8.881
< 0.1%
11.111
< 0.1%
17.951
< 0.1%
18.641
< 0.1%
19.261
< 0.1%
21.881
< 0.1%
ValueCountFrequency (%)
367.741
< 0.1%
354.551
< 0.1%
345.411
< 0.1%
3162
< 0.1%
3001
< 0.1%
2901
< 0.1%
2801
< 0.1%
272.221
< 0.1%
268.841
< 0.1%
260.871
< 0.1%

CV_SCR2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3387
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.27414722
Minimum0
Maximum328
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:41:45.233664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q177.44
median91.16
Q3105.1525
95-th percentile137.795
Maximum328
Range328
Interquartile range (IQR)27.7125

Descriptive statistics

Standard deviation27.20906072
Coefficient of variation (CV)0.2948719825
Kurtosis5.097385318
Mean92.27414722
Median Absolute Deviation (MAD)13.84
Skewness0.9053714702
Sum513967
Variance740.3329855
MonotonicityNot monotonic
2022-11-08T21:41:45.363664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100116
 
2.1%
7520
 
0.4%
111.1117
 
0.3%
85.7117
 
0.3%
12015
 
0.3%
112.515
 
0.3%
116.6714
 
0.3%
12514
 
0.3%
83.3313
 
0.2%
5012
 
0.2%
Other values (3377)5317
95.5%
ValueCountFrequency (%)
03
0.1%
1.891
 
< 0.1%
2.71
 
< 0.1%
4.551
 
< 0.1%
4.61
 
< 0.1%
4.961
 
< 0.1%
5.071
 
< 0.1%
6.781
 
< 0.1%
7.021
 
< 0.1%
7.171
 
< 0.1%
ValueCountFrequency (%)
3281
< 0.1%
322.731
< 0.1%
2881
< 0.1%
257.141
< 0.1%
244.641
< 0.1%
243.642
< 0.1%
2401
< 0.1%
230.771
< 0.1%
2302
< 0.1%
225.641
< 0.1%

CV_Varicela
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3425
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.48526032
Minimum0
Maximum328
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:41:45.493664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50.5435
Q177.8325
median91.365
Q3105.015
95-th percentile137.5
Maximum328
Range328
Interquartile range (IQR)27.1825

Descriptive statistics

Standard deviation27.06415219
Coefficient of variation (CV)0.2926320593
Kurtosis4.955909596
Mean92.48526032
Median Absolute Deviation (MAD)13.62
Skewness0.8816134481
Sum515142.9
Variance732.4683335
MonotonicityNot monotonic
2022-11-08T21:41:45.624662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100123
 
2.2%
12519
 
0.3%
8016
 
0.3%
133.3314
 
0.3%
85.7114
 
0.3%
7514
 
0.3%
12013
 
0.2%
84.6212
 
0.2%
88.8912
 
0.2%
105.5611
 
0.2%
Other values (3415)5322
95.5%
ValueCountFrequency (%)
05
0.1%
3.71
 
< 0.1%
4.181
 
< 0.1%
4.391
 
< 0.1%
5.411
 
< 0.1%
71
 
< 0.1%
7.021
 
< 0.1%
8.481
 
< 0.1%
8.891
 
< 0.1%
9.471
 
< 0.1%
ValueCountFrequency (%)
3281
< 0.1%
327.271
< 0.1%
2801
< 0.1%
253.061
< 0.1%
245.451
< 0.1%
2401
< 0.1%
230.771
< 0.1%
230.361
< 0.1%
225.851
< 0.1%
219.571
< 0.1%

CV_HEPatite_A
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3730
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190.197693
Minimum0
Maximum672.73
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:41:45.756666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile111.367
Q1162.26
median187.195
Q3212.77
95-th percentile277.6765
Maximum672.73
Range672.73
Interquartile range (IQR)50.51

Descriptive statistics

Standard deviation52.39975167
Coefficient of variation (CV)0.2755015103
Kurtosis6.277636744
Mean190.197693
Median Absolute Deviation (MAD)25.26
Skewness1.095050737
Sum1059401.15
Variance2745.733975
MonotonicityNot monotonic
2022-11-08T21:41:45.889663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200113
 
2.0%
177.7818
 
0.3%
187.517
 
0.3%
166.6715
 
0.3%
222.2214
 
0.3%
266.6714
 
0.3%
171.4314
 
0.3%
16014
 
0.3%
216.6713
 
0.2%
25013
 
0.2%
Other values (3720)5325
95.6%
ValueCountFrequency (%)
02
< 0.1%
2.821
< 0.1%
7.411
< 0.1%
8.621
< 0.1%
10.151
< 0.1%
15.061
< 0.1%
17.541
< 0.1%
21.621
< 0.1%
21.881
< 0.1%
25.141
< 0.1%
ValueCountFrequency (%)
672.731
< 0.1%
632.261
< 0.1%
598.551
< 0.1%
5681
< 0.1%
516.671
< 0.1%
5121
< 0.1%
510.21
< 0.1%
477.781
< 0.1%
476.921
< 0.1%
467.861
< 0.1%

Interactions

2022-11-08T21:41:40.211628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:21.511584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:23.082584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:24.746586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:26.305582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:27.933586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:29.422581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:31.018581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:32.467333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:34.033326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:35.523092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:37.127002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:38.602061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:40.325630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:21.685585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:23.200584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:24.861584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:26.416586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:28.045586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:29.533583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:31.127334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:32.578332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:34.145325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:35.636091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:37.236001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:38.714058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:40.444626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:21.806585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:23.320584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:24.988583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:26.539583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:28.163586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:29.650582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:31.240333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:32.696330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:34.266327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:35.754092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:37.351002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:38.832093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:40.560626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:21.926584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:23.439586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:25.125585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:26.658584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:28.277584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:29.768584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:31.352333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:32.809332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:34.382328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:35.870091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:37.467002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:38.947594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:40.677626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:22.041584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:23.560586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:25.243585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:26.776587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:28.390584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:29.884582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:31.463331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:32.926332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:34.497326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:35.985091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:37.580013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:39.060610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:40.793631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:22.160582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:23.678585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:25.360583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:26.890587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:28.504584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:29.998582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:31.575331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:33.037325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:34.611329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:36.100004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:37.695013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:39.180610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:40.911628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:22.277584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:23.807587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:25.481586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:27.022586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:28.621585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:30.114583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:31.688330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:33.148325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:34.725327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:36.215001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:37.809014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:39.297626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:41.025628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:22.388584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:23.923587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:25.598586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:27.136586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:28.733582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:30.223584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:31.797332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:33.258327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:34.838326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:36.325999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:37.919013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:39.529630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:41.137627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:22.500584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:24.038586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:25.716583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:27.247586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:28.846584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:30.333583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:31.904330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:33.364329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:34.950330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:36.559002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:38.030010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:39.641628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:41.253627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:22.615584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:24.157585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:25.844587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:27.360585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:28.960582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:30.565584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:32.018330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:33.593327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:35.063325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:36.673999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:38.143013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:39.754626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:41.372626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:22.733584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:24.278583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:25.962582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:27.589587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:29.076582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:30.679582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:32.127332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:33.703325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:35.177327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:36.789002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:38.258012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:39.869626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:41.487631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:22.851583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:24.507586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:26.077585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:27.703582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:29.193584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:30.791584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:32.236333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:33.813327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:35.291326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:36.901002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:38.371032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:39.983627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:41.605626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:22.965585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:24.628586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:26.190585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:27.817583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:29.308584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:30.905584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:32.349333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:33.923327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:35.404325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:37.013001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:38.484046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:41:40.095631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-08T21:41:46.011664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-08T21:41:46.155667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-08T21:41:46.302664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-08T21:41:46.558666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-08T21:41:46.706663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-08T21:41:41.776629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-08T21:41:41.945626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

CODMunicípioCV_BCGCV_HEPatite_BBCV_HIBCV_DPTCV_POLIOCV_ROTACV_PNEMOCV_MnCCCV_SCR1CV_SCR2CV_VaricelaCV_HEPatite_A
0110001110001 Alta Floresta D'Oeste90.19141.69141.69141.69147.1499.46156.68104.36123.4688.6487.41173.33
1110002110002 Ariquemes112.3297.9297.9297.92105.9093.41104.8096.36109.4884.3084.42169.81
2110003110003 Cabixi0.00152.50152.50152.50151.25138.75143.75148.75166.67165.43180.25355.56
3110004110004 Cacoal119.2163.6163.7663.9080.4985.0186.0386.39104.6385.2184.51169.31
4110005110005 Cerejeiras42.62137.55138.40137.55137.13125.74127.00137.97129.23120.00123.08250.77
5110006110006 Colorado do Oeste5.61118.69118.69118.69114.9584.58119.1690.19108.1562.6661.80132.19
6110007110007 Corumbiara22.05101.57101.57104.72102.3697.6499.21103.9496.0987.5089.06187.50
7110008110008 Costa Marques108.15127.90127.90127.90129.1893.56122.32132.62119.2688.9388.93171.31
8110009110009 Espigão D'Oeste84.89115.33115.33115.33124.44100.00123.5698.89119.1592.3490.85182.13
9110010110010 Guajará-Mirim88.3583.4883.4884.12100.9080.41139.6985.79152.1068.5863.38134.73

Last rows

CODMunicípioCV_BCGCV_HEPatite_BBCV_HIBCV_DPTCV_POLIOCV_ROTACV_PNEMOCV_MnCCCV_SCR1CV_SCR2CV_VaricelaCV_HEPatite_A
5560522160522160 Uruaçu90.8273.9873.9873.9883.1684.5289.2990.4896.9275.9177.90170.29
5561522170522170 Uruana68.4274.3475.6675.6698.68100.66105.92112.5097.2893.2092.52176.87
5562522180522180 Urutaí107.69192.31192.31192.31215.38192.31192.31176.92147.83126.09121.74278.26
5563522185522185 Valparaíso de Goiás80.4756.1256.1256.2080.3579.0873.5879.3585.8272.1364.42165.22
5564522190522190 Varjão84.38103.13103.13103.13121.88125.00125.00146.88162.07134.48134.48248.28
5565522200522200 Vianópolis98.4784.1885.7185.71101.02104.59105.10105.61106.8091.2690.78189.32
5566522205522205 Vicentinópolis98.2175.8975.8976.7993.75103.5799.1193.7591.9484.6885.48167.74
5567522220522220 Vila Boa50.00116.00116.00116.00114.00110.00118.00112.00130.36103.57101.79200.00
5568522230522230 Vila Propício94.3754.9354.9354.9373.2484.5195.7776.06126.9276.9271.15207.69
5569530010530010 Brasília96.9873.6674.4074.6688.6488.8792.4189.5286.5890.0688.88179.54